Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

Discursive approaches to language policy

This book brings together the fields of language policy and discourse studies from a multidisciplinary theoretical, methodological and empirical perspective. The chapters in this volume are written by international scholars active in the field of language policy and planning and discourse studies. The diverse research contexts range from education in Paraguay and Luxembourg via businesses in Wales to regional English language policies in Tajikistan. Readers are thereby invited to think critically about the mutual relationship between language policy and discourse in a range of social, political, economic and cultural spheres. Using approaches that draw on discourse-analytic, anthropological, ethnographic and critical sociolinguistic frameworks, the contributors in this collection explore and refine the ‘discursive’ and the ‘critical’ aspects of language policy as a multilayered, fluid, ideological, discursive and social process that can operate as a tool of social change as well as reinforcing established power structures and inequalities.

Introduction : why are discursive approaches to language policy necessary?

This volume is a series of explorations of language policy from a discursive perspective. Its chief aim is to systematically explore the interconnectedness of language policy and discourse through what we are terming ‘discursive approaches to language policy’ (DALP). We show that language policy is a multilayered phenomenon that is constituted and enacted in and through discourse (which is defined more closely in Sect. 1.2). Language policy is a fast-growing, vibrant, and interdisciplinary field of inquiry that offers a variety of theoretical frameworks, methodologies, analytic approaches, and empirical findings: the framing sections at the beginning of each part of this volume and the commentary at the end frame the discussion of developments in language policy and especially the role of DALP therein.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

Semantic relations: Modelling issues, proposals and possible applications

Semantic relations are an important element in the construction of ontology-based linguistic resources and models of problem domains. Nevertheless, they remain under-specified. This is a pervasive problem in both Software Engineering and Artificial Intelligence. Thus, we find semantic links that can have multiple interpretations, abstractions that are not enough to represent the relation richness of problem domains, and even poorly structured taxonomies. However, if provided with precise semantics, some of these problems can be avoided, and meaningful operations can be performed on them that can be an aid in the ontology construction process. In this paper we present some insightful issues about the representation of relations. Moreover, the initiatives aiming to provide relations with clear semantics are explained and the inclusion of their core ideas as part of a methodology for the development of ontology-based linguistic resources is proposed.

Bringing forward semantic relations: Issues and proposals

Semantic relations are an important element in the construction of ontologies and models of problem domains. Nevertheless, they remain fuzzy or under-specified. This is a pervasive problem in software engineering and artificial intelligence. Thus, we find semantic links that can have multiple interpretations in wide-coverage ontologies, semantic data models with abstractions that are not enough to capture the relation richness of problem domains, and improperly structured taxonomies. However, if relations are provided with precise semantics, some of these problems can be avoided, and meaningful operations can be performed on them. In this paper we present some insightful issues about the modeling, representation and usage of relations including the available taxonomy structuring methodologies as well as the initiatives aiming to provide relations with precise semantics. Moreover, we explain and propose the control of relations as a key issue for the coherent construction of ontologies.

URIs and Intertextuality: Incumbent Philosophical Commitments in the Development of the Semantic Web

Examines two commitments inherent in Resource Description Framework (RDF): intertextuality and rationalism. After introducing how rationalism has been studied in knowledge organization, this paper then introduces the concept of bracketed-rationalism. This paper closes with a discussion of ramifications of intertextuality and bracketed rationalism on evaluation of RDF.

Ontologies and the Semantic Web: Problems and Perspectives for LIS Professionals

Many years have passed since Berners-Lee envi- sioned the Web as it should be (1999), but still many information professionals do not know their precise role in its development, especially con- cerning ontologies –considered one of its main elements. Why? May it still be a lack of under- standing between the different academic commu- nities involved (namely, Computer Science, Lin- guistics and Library and Information Science), as reported by Soergel (1999)? The idea behind the Semantic Web is that of several technologies working together to get optimum information re- trieval performance, which is based on proper resource description in a machine-understandable way, by means of metadata and vocabularies (Greenberg, Sutton and Campbell, 2003). This is obviously something that Library and Information Science professionals can do very well, but, are we doing enough? When computer scientists put on stage the ontology paradigm they were asking for semantically richer vocabularies that could support logical inferences in artificial intelligence as a way to improve information retrieval systems. Which direction should vocabulary development take to contribute better to that common goal? The main objective of this paper is twofold: 1) to identify main trends, issues and problems con- cerning ontology research and 2) to identify pos- sible contributions from the Library and Information Science area to the development of ontologies for the semantic web. To do so, our paper has been structured in the following manner. First, the methodology followed in the paper is reported, which is based on a thorough literature review, where main contributions are analysed. Then, the paper presents a discussion of the main trends, issues and problems concerning ontology re- search identified in the literature review. Recom- mendations of possible contributions from the Library and Information Science area to the devel- opment of ontologies for the semantic web are finally presented.

Scheme Versioning in the Semantic Web

This paper describes a conceptual framework and meth- odology for managing scheme versioning for the Semantic Web. The first part of the paper introduces the concept of vocabulary encoding schemes, distinguished from metadata schemas, and discusses the characteristics of changes in schemes. The paper then presents a proposal to use a value record–similar to a term record in thesaurus management techniques–to manage scheme versioning challenges for the Semantic Web. The con-clusion identifies future research directions.

Using a Dialogue Manager to Improve Semantic Web Search

Question Answering systems that resort to the Semantic Web as a knowledge base can go well beyond the usual matching words in documents and, preferably, find a precise answer, without requiring user help to interpret the documents returned. In this paper, the authors introduce a Dialogue Manager that, through the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions, but also represents the structure of the discourse, including the user intentions and the questions context, adding the ability to deal with multiple answers and providing justified answers. The authors’ system performance is evaluated by comparing with similar question answering systems. Although the test suite is slight dimension, the results obtained are very promising.

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations

Using syntactic and semantic features for classifying mo- dal values in the portuguese language

This paper presents a study made in a field poorly explored in the Portuguese language – modality and its automatic tagging. Our main goal was to find a set of attributes for the creation of automatic tag- gers with improved performance over the bag-of-words (bow) approach. The performance was measured using precision, recall and F1. Because it is a relatively unexplored field, the study covers the creation of the corpus (composed by eleven verbs), the use of a parser to extract syntac- tic and semantic information from the sentences and a machine learning approach to identify modality values. Based on three different sets of attributes – from trigger itself and the trigger’s path (from the parse tree) and context – the system creates a tagger for each verb achiev- ing (in almost every verb) an improvement in F1 when compared to the traditional bow approach.

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations.